MSc Business Analytics & Artificial Intelligence

Advanced Data Analysis

The module provides an opportunity to pursue a more advanced area of analytics, building on the core module in Business Statistics.

Today, organisations and businesses collect and store information in data warehouses, and such information is available to be ‘mined' for improved management decision making. Some of that information can be analysed with simple statistics, but much of it requires more complex, statistical techniques. The module thus focuses on the use of a range of applied advanced data analysis techniques to convert information into knowledge.

Algorithms and Optimisation in Software Applications

The module will include coverage of the key technologies in the mathematical foundations of algorithms and optimisation, and the application of these techniques in a range of business settings.

Algorithms and optimisation are critical in most modern software applications, from artificial intelligence to personalisation. This module will give students a thorough grounding in key topics such as optimisation techniques, analysis of algorithms/computation complexity and data structures, all within the context of the software industry.

Deep Learning and AI Engineering

You’ll focus on deep learning architectures, AI engineering and strategic decision-making.

Applied Artificial Intelligence, the practical application of AI in business settings, largely translates to the application of deep learning architectures (neural networks), the engineering practices that support AI infrastructures (AI engineering) and the related business and strategic decisions required to specify the project. This module will bring a focus on all three of these to build upon the content of Analytics in Practice and Analytics Engineering, AI and Visualisation from your first term.

Generative AI and AI Applications

We will demystify generative AI and illuminate its crucial impact on finance.

You’ll gain insights into various generative models and hone Python and PyTorch skills. Through our pragmatic approach, students will apply key generative AI concepts like Generative Adversarial Networks, Transformer Models, Diffusion models and Natural Language Processing to real-world scenarios. As we round off the module, we will project into the future of Generative AI and engage in essential discussions on ethical implications. Our primary goal is to empower you with the know-how and aptitude to leverage Generative AI in finance, enhancing decision-making and pioneering innovative strategies.

Advanced Analytics: Models and Applications

Appreciate the power of analytics along with optimisation models to apply to them.

We’ll introduce advanced analytics using different optimisation models and demonstrate them with applications ranging from healthcare, sports, social network, to asset management and fraud detection.

Advanced Data Analysis

The module provides an opportunity to pursue a more advanced area of analytics, building on the core module in Business Statistics.

Today, organisations and businesses collect and store information in data warehouses, and such information is available to be ‘mined' for improved management decision making. Some of that information can be analysed with simple statistics, but much of it requires more complex, statistical techniques. The module thus focuses on the use of a range of applied advanced data analysis techniques to convert information into knowledge.

Deep Learning and AI Engineering

You’ll focus on deep learning architectures, AI engineering and strategic decision-making.

Applied Artificial Intelligence, the practical application of AI in business settings, largely translates to the application of deep learning architectures (neural networks), the engineering practices that support AI infrastructures (AI engineering) and the related business and strategic decisions required to specify the project. This module will bring a focus on all three of these to build upon the content of Analytics in Practice and Analytics Engineering, AI and Visualisation from your first term.

Prescriptive Analytics and Optimisation

This module will provide sound theoretical backgrounds through a variety of real-life applications of optimisation models in analytics and data analysis.

The module also includes an assignment that involves individually generated tasks to be solved by using computational technologies. The module aims to develop your interest, knowledge and understanding of various optimisation models to support decision making in organisations. You’ll learn about the theoretical underpinnings of these models as well as how they are used in applications, as you gain practical experience in modelling and problem solving.

Developing an Analytics Capability

This module explores the concept of developing analytics capability across two dimensions.

Firstly, it focuses on the organisational challenge of developing an analytics function within the organisation, and implementing a data literate culture. Secondly, the module incorporates an individual's ability to develop their own analytics competencies, including topics such as an analytical mindset, reflexive practice and the ability to integrate one's work with organisational practice.

Financial Analytics

The module provides an introduction to financial analytics by applying various (analytical, statistical and mathematical) techniques to financial market and investment portfolios.

As an important aspect of financial analytics we introduce various modelling and solving approaches for decision making problems arising in finance. We look at the analytics process from data management, modelling and computational tools perspectives. We will review various widely used approaches to portfolio analytics and introduce modelling approaches for portfolio analytics system design. The theoretical and practical aspects of various approaches will be motivated through underlying financial decision-making problems using the financial market data and complexities of financial decision making problems will be addressed.

Prescriptive Analytics and Optimisation

This module will provide sound theoretical backgrounds through a variety of real-life applications of optimisation models in analytics and data analysis.

It includes an assignment that involves individually generated tasks to be solved by using computational technologies. The module aims to develop your interest, knowledge and understanding of various optimisation models to support decision making in organisations. You’ll learn about the theoretical underpinnings of these models as well as how they are used in applications, as you gain practical experience in modelling and problem solving.

Supply Chain Analytics

This module provides an introduction to how analytics and operational research can be used in supply chains.

It focuses on both the structural and operational elements of supply chain management: the design of supply networks as well as scheduling, inventory control, quality control and vehicle routing. We’ll introduce various (analytical, mathematical and statistical) techniques to analyse supply chain performance and to identify inefficiencies and risk factors in operational, managerial and financial aspects of a supply chain.

Algorithms and Optimisation in Software Applications

The module will include coverage of the key technologies in the mathematical foundations of algorithms and optimisation, and the application of these techniques in a range of business settings.

Algorithms and optimisation are critical in most modern software applications, from artificial intelligence to personalisation. This module will give students a thorough grounding in key topics such as optimisation techniques, analysis of algorithms/computation complexity and data structures, all within the context of the software industry.

Developing an Analytics Capability

This module explores the concept of developing analytics capability across two dimensions.

Firstly, it focuses on the organisational challenge of developing an analytics function within the organisation, and implementing a data literate culture. Secondly, the module incorporates an individual's ability to develop their own analytics competencies, including topics such as an analytical mindset, reflexive practice and the ability to integrate one's work with organisational practice.

Generative AI and AI Applications

We will demystify generative AI and illuminate its crucial impact on finance.

You’ll gain insights into various generative models and hone Python and PyTorch skills. Through our pragmatic approach, students will apply key generative AI concepts like Generative Adversarial Networks, Transformer Models, Diffusion models and Natural Language Processing to real-world scenarios. As we round off the module, we will project into the future of Generative AI and engage in essential discussions on ethical implications. Our primary goal is to empower you with the know-how and aptitude to leverage Generative AI in finance, enhancing decision-making and pioneering innovative strategies.

Marketing and Digital Analytics

Marketing and Digital Analytics will provide participants with a range of analytical applications and methods within the context of marketing and digital domains.

The module will incorporate a variety of cutting-edge techniques including artificial intelligence, social media analytics, channel optimisation and a range of other applications. These will be presented within the context of the current challenges and opportunities within the marketing domain.

Exposure to the global economy brings new ideas, technologies, and management practices to a business. Whether looking to build a new plant, sell to a new market or find global business partners, exploiting opportunities in new markets or regions poses fresh challenges.

Many of those challenges arise from the different business environment that's present in countries around the world. What does an understanding of economics tell us about which countries and regions will see markets develop quickly in the medium term? Which governments act in ways that pose macroeconomic risks and which do not? How can resources and intellectual property be managed effectively? Which economic theories offer strategies to foster talent and help staff respond to new opportunities?

The patterns of world trade and the responses of governments have always been in constant flux. Businesses can't rely on traditional markets in the medium- to long-term. The pandemic has disturbed even the recently-established patterns of global economic growth. Strategic business decisions require an appreciation of the economic thinking that underpins these new patterns of growth and the changes in international finance that support them.

This module will provide real world examples and case studies to illustrate how economic thinking helps businesses make the most of new opportunities, while managing the risks associated with expansion into new markets and regions.

The module has been developed for business and management students across the WBS MSc portfolio and draws on ideas and research from macroeconomics, microeconomics, international finance, business strategy and development economics. No previous experience of economic analysis is required.

The module aims to provide training in applying data science methods to contemporary economic problems by looking at case studies. Specifically, the module aims to give you the opportunity to conduct a data science project using economic data. You will discover how to replicate and extend a data science case. The module aims to give the appropriate grounding in economic analysis, while developing your research and communication skills.

The principle aims of the module are to:

• Gain with a practical and theoretical understanding of how the financial services industry has been transformed through the adoption and use of digital innovation and information technologies
• Familiarise yourself with the key technical, procedural, and business aspects of relevant financial technologies and FinTech innovations
• Get with the right mix of technical and business skills to manage IT change in a highly complex and distributed but also competitive setting in finance
• Learn how recent developments in fintech, paytech, regtech, and insuretech (e.g. open banking, blockchain and shared ledger technologies, cryptocurrencies, artificial intelligence, security, etc.) will affect the future of finance.

Focus on how digital technologies enable organisations to offer digital services that aim at improving customer experience as well as firm performance. You'll be prepared to engage with technology-related conversations in the modern workplace.

The module has three themes. The first theme is gaining competitive advantage using a digital business strategy and covers concepts such as motivations for digital transformation. The second theme provides an overview of what today’s managers need to know about the advantages and disadvantages of cutting-edge technologies including artificial intelligence (AI), virtual reality, internet of things (IoT), cloud computing, social media, and data analytics. The last theme focuses on the lifecycle of digital service provision, covering key concepts such as technology selection and sourcing, agile implementation, and risk management

There is a large and growing literature in marketing on the understanding and forecasting of the growth in demand for new products.

A parallel but often unconnected literature exists in strategic management and entrepreneurship on the source and drivers of innovation. Entrepreneurship is the managerial process for creating and managing innovation. Without entrepreneurship, systematic innovation will not take place.

Therefore, this module will address issues relating to the management, adoption and spreading of innovation and new technologies from a multidiscipline perspective. It takes a dynamic and global perspective by examining the latest development/debate on the role of the emerging markets, particularly China, and the role of government and institutional environment in the global innovation landscape.

This module puts particular emphasis on corporations’ responsibilities in global society and discusses key governance issues related to this agenda. Firms will be seen as both 'takers' and 'givers' of governance and regulation in a variety of formal and informal ways.

The module is not a traditional course in business ethics, but explores the influence of the non-market environment on businesses’ strategies and operational practices and will confront some 'critical issues' topics. You will learn to demonstrate comprehensive understanding of the responsibilities of companies in the local and global context as well as understanding of the significance of the non-market environment for business decisions.

The aim of this module is to provide you with a deep understanding of this strategic role of human capital management and the tools with which to analyse and manage it.

We will consider what is means to align the HR function with strategic objectives. We will ask whether all human capital is equally valuable. We will debate the extent to which human capital and its associated outcomes are quantifiable in a meaningful way. We will examine whether HRM can support both efficiency and flexibility at the same time. Ultimately, the class will provide a range of models and tools for managing human capital in a strategic way.

Critically discuss a selection of current issues in digital strategy and agile transformation whilst you enhance your critical thinking and creativity in individual and group settings. This module will enhance your skills in analysing complex, real-life strategic problems.

From the shop-floor to the boardroom the ways individuals and teams employ digital technology to execute strategy can make the difference between success and failure. Using the latest strategy learning techniques used in leading firms and consultancies (business simulations, interactive workshops, live video case studies, apprentice-style challenges), this module will take you on a learning journey. At the end of the module, through analysis and immersion in real-life strategy situations, you will develop a toolkit to help grow as competent strategists and deal with agile transformations. These skills are essential for a career in consultancy or strategy roles.

To be effective in modern organisations requires an understanding of the dynamics of digital work. This module will equip you with a broad understanding of the tools and organisational arrangements driving this shift towards digital working, and provide you with techniques and frameworks to operate effectively in this more dynamic and fluid work environment.

The module reviews major trends and changes in the workplace arising from the increased adoption of digital tools to support work practices in modern organisations. It reviews major drivers of adoption of these tools and the emergence of new dynamics of interactions and patterns of work. 

Entrepreneurship is not only about new ventures or 'small' business or 'early-stage' business, nor even exclusively about for-profit business. Rather, it is a method of creative problem solving in business and a logic for taking action in a variety of settings where business can make a positive difference. The module, therefore, will explore a range of different ways of thinking about what entrepreneurship is and what entrepreneurs do.

Our intention in the module is to try and help facilitate you in developing a more entrepreneurial mindset. This module also provides you with an opportunity to develop a Feasibility Study for exploiting an entrepreneurial opportunity - a precursor to a formal Business Plan.

Gain an introduction to financial analytics by applying various techniques, including analytical, statistical, and mathematical, to financial market and investment portfolios. You will be introduced to various modelling and solving approaches to decision making problems arising in finance. 

You will look at the analytics process from data management, modelling, and computational tools perspectives, and review various widely used approaches to portfolio analytics, whilst being introduced to modelling approached for portfolio analytics system design.

You will also address the theoretical and practical aspects of various approaches, considering financial market data and the complexities of financial decision making problems. 

This module provides an introduction to current quantitative forecasting methods, and its overall aim is to develop practical competence in their use. The module concentrates on models for short term forecasting, as these illustrate all the basic principles of analysing, comparing and extrapolating different models.

The module will provide you a robust understanding of various generative models, while fostering proficiency in Python and TensorFlow to handle financial data.

You will learn to apply important generative AI topics such as Autoencoders, Generative Adversarial Networks, the attention mechanism, Transformer models, and Natural Language Processing. You will examine these technologies not only from a theoretical standpoint but also in practical financial scenarios, such as fraud detection, financial forecasting, asset pricing, and algorithmic trading.

Lastly, the module wraps up with a forward-looking discussion on the prospects and ethical dimensions of Generative AI in finance. The overarching aim is to equip you with the knowledge and skills necessary to apply Generative AI solutions in finance, leading to more informed decision-making and innovative approaches. This module builds on the learning from Programming and Big Data Analytics modules.

The module aims to develop a critical awareness of the issues, problems, concepts, and skills associated with the development of international business strategy and operations. Topics will include the drivers and processes of internationalisation, cross-cultural management challenges, the assessment of overseas investment opportunities and the specific challenges of emerging markets.

This module aims to develop a greater understanding of how to foster an innovative work environment. In this module the role of the leader will be examined as he/she facilitates innovative ventures by identifying and defining problems worth pursuing (where potential payoffs justify risks), creating a context that allows for multiple parties to work together in generating viable ideas (those with a chance of successful implementation) and managing the context of idea development and fielding – to ensure that viable ideas are likely to be adopted in the marketplace. This module also aims to understand how to develop leaders for innovation.

 It is further aimed to develop an understanding into issues such as:

•  Why is innovation important (especially now)?
•  What makes a leader more or less successful at leading for innovation?
•  What is the role of the team in the innovation process?
•  How might leading for innovation vary by domain (i.e. IT, marketing, etc)?
•  How can crowdsourcing be used to foster innovation?

The module will provide the conceptual understanding, practical skills and experience in using programming tools for you to model and analyse demand, and to use the outcomes to optimise pricing or product availability decisions in an automated fashion.

Pricing Analytics focuses on how a company should set and update pricing and product availability decisions across its various selling channels in order to maximise its profitability in an automated fashion. In other words, we are concerned with algorithms making real-time pricing decisions, rather than strategic pricing.

This pathway suits students with an interest in conducting in-depth analytics projects but in an applied setting rather than a research setting (the Research Paper pathway). You will structure analytics projects, perform hands-on analysis of data, interpret results in the context of the business domain, and develop a visual portfolio of your work.

Incorporating an innovative and exciting team business simulation, you’ll gain an understanding of each business function through a series of real-world play activities, and learn to evaluate business performance through various functional dimensions.

You’ll replicate how a team would work together in a real business, giving you a chance to put all of the learning from your MSc year into practice. Following the simulation you will complete an Individual Reflection and a Business Analysis.

This course offers the opportunity to self-source a summer internship, to increase your employability by applying your theoretical learnings to a real-world industry setting. Our careers team will support you through each step of the process.

Apply the knowledge you've gained on the course with a research paper. Your investigation of a contemporary topic will develop your theoretical and practical understanding of current problems in this area, as well as your research and communication skills. This is your opportunity to demonstrate how you would work in reality, as you detail the decisions you would make – and why.